initial version

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelWithLMHead
+
+tokenizer = AutoTokenizer.from_pretrained("mmcquade11/autonlp-reuters-summarization-34018133")
+model = AutoModelWithLMHead.from_pretrained("mmcquade11/autonlp-reuters-summarization-34018133")
+
+def summarize(text):
+    input_ids = torch.tensor(tokenizer.encode(text, add_special_tokens=True)).unsqueeze(0)
+    summary_ids = model.generate(input_ids, num_beams=4, max_length=100, early_stopping=True)
+    return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+
+def summarize_text(text):
+    return summarize(text)
+
+iface = gr.Interface(summarize_text, "textbox", "label")
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

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+torch
+transformers